ABSTRACTPurpose. To evaluate correlation between tomographic gradation of keratoconus (KC) and its corresponding air-puff induced biomechanical response. Methods. Corneal tomography and biomechanics were measured with Scheimpflug imaging in 44 normal and 92 KC corneas. Deformation waveform was also analyzed with Fourier series. A custom KC severity scale was used from 1 to 3 with 3 as the most severe grade. Tomographic and biomechanical variables were assessed among the grades. Sensitivity and specificity of the variables were assessed using receiver operating characteristics (ROC). Results. Curvature variables were significantly different between normal and disease (P < 0.05) and among grades (P < 0.05). Biomechanical variables were significantly different between normal and disease (P<0.05) but similar among grades 1 and 2 (P > 0.05). All variables had an area under the ROC curve greater than 0.5. The root mean square of the Fourier cosine coefficients had the best ROC (0.92, cut-off: 0.027, sensitivity: 83%, specificity: 88.6%). Spearman correlation coefficient was significant between most variables (P < 0.05). However, tomographic segregation of keratoconus did not result in concomitant biomechanical segregation of the grades. Conclusions. There was lack of significant biomechanical difference between mild disease grades, despite progressive corneal thinning. Mathematical models that estimate corneal modulus from air-puff deformation may be more useful.

Mentions:
The correlation between all variables was assessed with the Spearman correlation coefficient (Table 2). Most of the correlations were statistically significant (P < 0.05). Keratometry correlated well with all biomechanical variables (P < 0.001). Interestingly, AUDA and an RMS had a very high correlation (0.945 and 0.919) with DA. As an example, Figures 4(a), 4(b), 4(c), and 4(d) show the linear regression of AUDA and DA with Kmean and TPT. Both AUDA and DA had a significantly negative correlation with Kmean and TPT. Figure 5 shows the correlation between AUDA and DA using all the grades. Table 3 lists the results from the ROC analyses. Time had the least area under the ROC curve equal to 0.511 with a sensitivity and specificity of 70.5% and 62.8%, respectively. Since keratometry was used for gradation, it had the highest area under the ROC curves among all variables (greater than 0.9). Among the biomechanical variables, an RMS had the best area under the ROC curve equal to 0.915 with sensitivity and specificity of 83% and 88.6%, respectively. AUDA was a close second (area under the ROC curve = 0.886, sensitivity = 73.9%, specificity = 93.2%). Figure 6 shows an overlay of DA of four corneas, one from each grade, with CCT and IOP reported next to the grade label. From Figure 6, salient observations relative to grade 0 were as follows: (a) quicker increase in DA in the first half of the applanation test in higher disease grades; (b) DA was greater in higher disease grades; (c) slower decrease in DA in the second half of the applanation test in higher disease grades; (d) globe deformation was similar in all the corneas.

Mentions:
The correlation between all variables was assessed with the Spearman correlation coefficient (Table 2). Most of the correlations were statistically significant (P < 0.05). Keratometry correlated well with all biomechanical variables (P < 0.001). Interestingly, AUDA and an RMS had a very high correlation (0.945 and 0.919) with DA. As an example, Figures 4(a), 4(b), 4(c), and 4(d) show the linear regression of AUDA and DA with Kmean and TPT. Both AUDA and DA had a significantly negative correlation with Kmean and TPT. Figure 5 shows the correlation between AUDA and DA using all the grades. Table 3 lists the results from the ROC analyses. Time had the least area under the ROC curve equal to 0.511 with a sensitivity and specificity of 70.5% and 62.8%, respectively. Since keratometry was used for gradation, it had the highest area under the ROC curves among all variables (greater than 0.9). Among the biomechanical variables, an RMS had the best area under the ROC curve equal to 0.915 with sensitivity and specificity of 83% and 88.6%, respectively. AUDA was a close second (area under the ROC curve = 0.886, sensitivity = 73.9%, specificity = 93.2%). Figure 6 shows an overlay of DA of four corneas, one from each grade, with CCT and IOP reported next to the grade label. From Figure 6, salient observations relative to grade 0 were as follows: (a) quicker increase in DA in the first half of the applanation test in higher disease grades; (b) DA was greater in higher disease grades; (c) slower decrease in DA in the second half of the applanation test in higher disease grades; (d) globe deformation was similar in all the corneas.

ABSTRACTPurpose. To evaluate correlation between tomographic gradation of keratoconus (KC) and its corresponding air-puff induced biomechanical response. Methods. Corneal tomography and biomechanics were measured with Scheimpflug imaging in 44 normal and 92 KC corneas. Deformation waveform was also analyzed with Fourier series. A custom KC severity scale was used from 1 to 3 with 3 as the most severe grade. Tomographic and biomechanical variables were assessed among the grades. Sensitivity and specificity of the variables were assessed using receiver operating characteristics (ROC). Results. Curvature variables were significantly different between normal and disease (P < 0.05) and among grades (P < 0.05). Biomechanical variables were significantly different between normal and disease (P<0.05) but similar among grades 1 and 2 (P > 0.05). All variables had an area under the ROC curve greater than 0.5. The root mean square of the Fourier cosine coefficients had the best ROC (0.92, cut-off: 0.027, sensitivity: 83%, specificity: 88.6%). Spearman correlation coefficient was significant between most variables (P < 0.05). However, tomographic segregation of keratoconus did not result in concomitant biomechanical segregation of the grades. Conclusions. There was lack of significant biomechanical difference between mild disease grades, despite progressive corneal thinning. Mathematical models that estimate corneal modulus from air-puff deformation may be more useful.